Engineering 3D parallelized microfluidic droplet generators with equal flow profiles by computational fluid dynamics and stereolithographic printing
Bibliographic record
Abstract
Microfluidic droplet generators excel in generating monodisperse micrometer-sized droplets and particles. However, the low throughput of conventional droplet generators hinders their clinical and industrial translation. Current approaches to parallelize microdevices are challenged by the two-dimensional nature of the standard fabrication methods. Here, we report the facile production of three-dimensionally (3D) parallelized microfluidic droplet generators consisting of stacked and radially multiplexed channel designs. Computational fluid dynamics simulations form the design basis for a microflow distributor that ensures similar flow rates through all droplet generators. Stereolithography is the selected technique to fabricate microdevices, which enables the manufacturing of hollow channels with dimensions as small as 50 μm. The microdevices could be operated up to 4 bars without structural damage, including deformation of channels, or leakage of the on-chip printed Luer-Lok type connectors. The printed microdevices readily enable the production of water-in-oil emulsions, as well as polymer containing droplets that act as templates for both solid and core-shell hydrogel microparticles. The cytocompatibility of the 3D printed device is demonstrated by encapsulating mesenchymal stem cells in hydrogel microcapsules, which results in the controllable formation of stem cell spheroids that remain viable and metabolically active for at least 21 days. Thus, the unique features of stereolithography fabricated microfluidic devices allow for the parallelization of droplet generators in a simple yet effective manner by enabling the realization of (complex) 3D designs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".